X-ray chest image is widely applied in the diagnosis of pulmonary disease such as pneumonia, phthisis, lung cancer and etc. Therein, the death toll of lung cancer is the highest one among the death tolls of all cancers. If the pulmonary disease can be detected in early phase, the therapeutic effect of the disease will be improved while the death rate of cancers will be reduced. Although the detection performance of X-ray chest image is not as good as that of CT, it has high economical efficiency and low radiation dosage. Thus, the X-ray chest image is still the major detection detection means at present.
X-ray chest imaging is the image of different intensity that is formed on the ray receiving surface based on the differences of density and thickness to different tissues in the lung which make the absorption degrees different during the X-ray penetration process. As the ray is projected to a two-dimensional surface, different tissues on the ray direction will be displayed on the image with overlap, which will result in the hard observation and recognition of the partial area of lesion. Ghost image is an inherent problem of X-ray chest image, which is also the main reason for the misdiagnosis making to many pulmonary diseases. The researches show that the misdiagnosis rate for the chest image diagnosis of the doctors reaches 30% and 82%-95% of missed diagnosis therein is resulted from the overlap or occlusion of the rib on the soft tissue of lung. The conventional method of gaining the image with the soft tissue of lung only is the dual energy difference technology, which uses the dedicated device to conduct the exposure perspective with two times of different energy on the people in detection and separates the rib and the soft tissue, which needs larger radiation dosage and is just confined to the institutions like hospital.
In recent years, the researchers utilize digital image processing technique to solve the ghost image problem of the chest image and put forward some solutions. For example, Giger et al. propose a kind of image difference technology which improves the detection rate of lung nodule (Medical Physics, vol. 17, pp. 861-865, 1990.). Keserci et al, design a kind of filter to conduct filtering on the image, which eliminates the influence of rib by restraining the oblong objects in the image and strengthens the circle objects to extrude the lung nodule in the image (Medical Image Analysis, vol. 6, pp. 431-447, 2002). Loog et al. put forward a kind of general filter framework based on regression to restrain the skeletal structure, which learns the filter by relying on the training image and obtains the soft tissue image through reconstruction of the testing images (Computer Vision Approaches to Medical Image Analysis, New York: Springer, 2006, vol. 4241, Lecture Notes in ComputerScience, pp. 166-177.). Suzuki et al. utilize artificial neural network to restrain the rib and strengthen the lung nodule (IEEE Trans. Med. Imaging, 2006, 25(4): 406-416.). Lee et al. Eliminate the rib through segmenting out the rib and adopting genetic algorithm to optimize a contrast model (Computers & Mathematics with Applications, 2012, 64(5):1390-1399.). In general, these methods can be divided into the implicit and explicit rib suppression. Therein, the implicit methods conduct suppression after positioning the location of the rib and need to conduct accurate segmentation on the rib while the implicit methods need a large quantity of samples for training to build the regression model, which are not easy for the clinical application.